RevOps · GTM Engineering · Applied AI

Revenue operations, built with governed AI.

I build the CRM, attribution, forecasting, and automation your revenue team runs on, and the governed AI layer that keeps every number accurate. Fractional RevOps for B2B teams that need a working system, not slideware.

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Trained & certified through Anthropic Academy ● AI Fluency for Builders (2026) ● Claude Code in Action (2026)

What I do

Three things, done so they hold up.

Foundation

RevOps infrastructure

HubSpot and Salesforce architecture, attribution, forecasting, pipeline, and reporting leadership can bet on. I make the data trustworthy first, then build on it.

Leverage

Governed AI agent systems

AI that does real operational work without shipping wrong data. A generator proposes, an independent verifier checks it against source and fails closed. Fast, and safe by construction.

Scale

GTM automation

Outbound, enrichment, routing, and the repeatable systems that turn one-off fire drills into durable infrastructure, so your team runs more plays without adding headcount.

The difference

Most AI in revenue ops ships confident, wrong answers. Mine doesn't.

I separate the model that generates from the check that approves, so nothing reaches a decision unverified. Every number traces to a dated source. A human stays in the loop on anything that sends a message or changes data. That is what governed means here: the automation is fast because the guardrails are built in, not bolted on.

under-reported revenue surfaced by rebuilding a client's attribution, changing how they hired and spent

~$150K/yr

of tooling and outsourced admin replaced with governed in-house automation

300+

governed AI agents built on a human-in-the-loop, fail-closed pattern

See it work

Proof you can run, not just claims.

CRM Trust Audit

A governed data-quality audit that shows exactly which records are corrupting your forecast, routing, and reporting, ranked by impact and traced to the record. Runs on your machine, reads only. The report shown here is its real output on a sample CRM.

Runnable verifier demo

A working example that catches a hallucinated number, a stale signal, and a fabricated entity, and refuses to pass them. Live walkthrough available on request.

◆ CRM Trust Audit — data_quality_report.mdread-only

60 records scanned 58 issues found 26 HIGH severity

HIGHRecords owned by a departed user9

HIGHRecords with no owner7

HIGHDeals corrupting the forecast7

MEDStale open deals past close date8

MEDDuplicate companies & contacts6

Every figure computed directly from the data. Every flag traced to the record.

Who I work with

B2B revenue teams that need a system built, not administered.

Early-stage to scaling companies, with real depth in healthcare and other regulated, detail-heavy verticals where accuracy is not optional.

Led by Danny Danczewski. A RevOps and GTM operator who came up as a top-quota SDR and team lead, then became the person who builds the revenue machine end to end. The last two years, deep in governed, applied AI. I architect and direct the build, verify the output, and keep a human in the loop before anything ships.

Anthropic Academy certified: AI Fluency for Builders · Claude Code in Action (2026)